Recent studies have demonstrated that neural implants for neural prosthetic applications can help paralyzed patient populations by allowing control of external devices. We have recently demonstrated that neural signals recorded from the posterior parietal cortex (PPC) of a tetraplegic subject provides a valuable source of neural signals for prosthetic control. Previous studies have demonstrated the utility of using signals from human motor cortex (M1). In light of these findings, we propose simultaneous implants in M1 and PPC to answer the important question of how these two brain areas compare in helping the patient population. To compare brain areas, we believe it is essential to use rigorous testing paradigms that are able to address fundamental questions of how intentions are coded in the two brain areas. Moreover, the experiments proposed will be in many cases the first studies examining the properties and complexities of the representation of intentions. In Aim 1a we will thus compare how the two brain areas code high-level goals and instantaneous execution signals. Aim 1b will test how these intention signals generalize across multiple contexts. Aim 2 will test the reference frames of intention signals, e.g. whether goal signals are represented with respect to where the subject is looking, the current location of the effector, or the body or world Meaningful behaviors are most often the result of combinations of movements sequenced in time, and therefore we will test in Aim 3 how neural representations of intentions are combined and sequenced. Basic scientific explorations of these properties will be leveraged to enable typing interfaces and the control of a tablet computer. Our proposal not only allows us to test whether one brain area is better than the other for particular motor variables but also whether they provide complimentary types of information. Decoding algorithms produce actions by interpreting neural activity. The proposed studies will provide seminal data on how intentions are coded in the two areas, thus informing how the next generation of decoding algorithms should be designed by providing a better understanding of how neural signals should be interpreted. Clinical relevance: the number of patients suffering from some form of paralysis in the United States alone has been estimated to be as high as 5.6 million. Paralysis can result from spinal cord injury, traumatic brain injury, stroke, peripheral neuropathies, and neurodegenerative disorders such as amyotrophic lateral sclerosis and multiple sclerosis. Another 2 million patients have motor disabilities due to limb amputation. The current application will compare two prominent areas for recording prosthetic controls signals to determine their similarities and differences in applicability to prosthetics. This research will enable improved design of neuroprosthetics that can use the complementary signals found in these two areas to maximum advantage.